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A suite of inflation forecasting models

Author

Listed:
  • Luis J. Álvarez

    (Banco de España)

  • Isabel Sánchez

    (Banco de España)

Abstract

This paper describes the econometric models used by the Banco de España to monitor consumer price inflation and forecast its future trends. The strategy followed heavily relies on the results from a set of econometric models, supplemented by expert judgment. We consider three different types of approaches and highlight the relevance of heterogeneity in price-setting behaviour and the importance of using models that allow for a slowly evolving local mean when forecasting inflation.

Suggested Citation

  • Luis J. Álvarez & Isabel Sánchez, 2017. "A suite of inflation forecasting models," Occasional Papers 1703, Banco de España.
    • Handle: RePEc:bde:opaper:1703
    as

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    File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosOcasionales/17/Fich/do1703e.pdf
    File Function: First version, February 2017
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    References listed on IDEAS

    as
    1. Joseph McGillicuddy & Lowell R. Ricketts, 2015. "Is Inflation Running Hot or Cold?," Economic Synopses, Federal Reserve Bank of St. Louis, issue 16.
    2. Luis J. Álvarez & Emmanuel Dhyne & Marco Hoeberichts & Claudia Kwapil & Hervé Le Bihan & Patrick Lünnemann & Fernando Martins & Roberto Sabbatini & Harald Stahl & Philip Vermeulen & Jouko Vilmunen, 2006. "Sticky Prices in the Euro Area: A Summary of New Micro-Evidence," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 575-584, 04-05.
    3. Mario Izquierdo & Juan Francisco Jimeno, 2015. "Employment, wage and price reactions to the crisis in spain: firm-level evidence from the wdn survey," Occasional Papers 1503, Banco de España.
    4. Matheson, Troy & Stavrev, Emil, 2013. "The Great Recession and the inflation puzzle," Economics Letters, Elsevier, vol. 120(3), pages 468-472.
    5. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
    6. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    7. Marianna Riggi & Fabrizio Venditti, 2015. "Failing to Forecast Low Inflation and Phillips Curve Instability: A Euro-Area Perspective," International Finance, Wiley Blackwell, vol. 18(1), pages 47-68, March.
    8. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    9. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    10. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    11. Jonathan H. Wright, 2013. "Evaluating Real‐Time Var Forecasts With An Informative Democratic Prior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 762-776, August.
    12. Álvarez, Luis J. & Hurtado, Samuel & Sánchez, Isabel & Thomas, Carlos, 2011. "The impact of oil price changes on Spanish and euro area consumer price inflation," Economic Modelling, Elsevier, vol. 28(1), pages 422-431.
    13. Luis Julián Álvarez & Alberto Urtasun, 2013. "Variation in the cyclical sensitivity of Spanish inflation: an initial approximation," Economic Bulletin, Banco de España, issue JUL, pages 11-17, July-Augu.
    14. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    15. Luis Julián Álvarez & Alberto Cabrero & Alberto Urtasun, 2014. "A procedure for short-term GDP forecasting," Economic Bulletin, Banco de España, issue OCT, pages 29-35, October.
    16. James H. Stock & Mark W. Watson, 2010. "Modeling inflation after the crisis," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
    17. Sharon Kozicki & P. A. Tinsley, 2012. "Effective Use of Survey Information in Estimating the Evolution of Expected Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 145-169, February.
    18. repec:zbw:bofrdp:2014_031 is not listed on IDEAS
    19. repec:bde:journl:v:07-08:y:2013:p:09 is not listed on IDEAS
    20. Luis Julián Álvarez & Ana Gómez Loscos & Alberto Urtasun, 2015. "Asymmetries in the relationship between inflation and activity," Economic Bulletin, Banco de España, issue NOV, pages 3-9, November.
    21. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Nadiia Shapovalenko, 2021. "A Suite of Models for CPI Forecasting," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 252, pages 4-36.

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    More about this item

    Keywords

    inflation; forecasting; Phillips curves; transfer functions; judgemental forecasts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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